Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation Networks (1911.05075v2)

Abstract: In the semantic segmentation of street scenes with neural networks, the reliability of predictions is of highest interest. The assessment of neural networks by means of uncertainties is a common ansatz to prevent safety issues. As in applications like automated driving, video streams of images are available, we present a time-dynamic approach to investigating uncertainties and assessing the prediction quality of neural networks. We track segments over time and gather aggregated metrics per segment, thus obtaining time series of metrics from which we assess prediction quality. This is done by either classifying between intersection over union equal to 0 and greater than 0 or predicting the intersection over union directly. We study different models for these two tasks and analyze the influence of the time series length on the predictive power of our metrics.

• The paper introduces a novel time-dynamic framework leveraging video data and temporal dynamics to quantify and enhance the reliability of deep semantic segmentation networks in safety-critical applications.
• It proposes a light-weight segment tracking algorithm to monitor semantic segments across frames, enabling the generation of time-series metrics for individual objects.
• Empirical validation on VIPER and KITTI datasets demonstrates that the time-dynamic approach significantly improves meta classification and regression performance compared to single-frame methods, with reported AUROC up to 86.01% and R2 up to 85.82% on VIPER.

Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation Networks

This paper presents a sophisticated approach to enhancing the reliability of deep semantic segmentation networks, specifically targeting applications in safety-critical domains such as automated driving. Such applications necessitate a high degree of prediction reliability, which can be achieved through effective uncertainty quantification. While previous studies have predominantly focused on uncertainty quantification based on single-frame analysis, this paper introduces a novel time-dynamic perspective, leveraging the availability of video data to improve prediction quality assessments.

Core Contributions

1. Time-Dynamic Metrics: The authors propose an innovative framework to assess the prediction quality of neural networks by incorporating temporal dynamics into uncertainty quantification. This approach involves tracking predicted segments over video frames, thereby generating time series of metrics that capture the dynamics of predicted objects. These time-dependent metrics are used to perform meta classification and meta regression tasks, particularly focusing on the intersection over union (IoU) measure.
2. Segment Tracking Algorithm: A significant contribution is the light-weight tracking algorithm designed to monitor semantic segments over time. This algorithm matches segments in consecutive frames based on geometric characteristics and overlap, adapting to the movement of objects within scenes. The method excels in handling the temporal continuity of detected segments, addressing challenges posed by occlusion and segment fragmentation.
3. Meta Classification and Regression: By employing segment-wise metrics derived from the network's softmax outputs, the authors predict the IoU of each segment. This dual-task setup involves determining whether a segment intersects with the ground truth (meta classification) and predicting the exact IoU value (meta regression). The paper explores various models for these tasks, including logistic regression, gradient boosting, and neural networks, providing insights into the advantages of integrating time-series information.
4. Empirical Validation: The authors validate their methodology on two prominent datasets: the synthetic VIPER dataset and the real-world KITTI dataset. Through extensive experimentation, they demonstrate the superiority of their time-dynamic approach over single-frame methods, achieving significant improvements in both meta classification and regression performance metrics. For instance, they report AUROC values up to 86.01% and $R^2$ values up to 85.82% for the VIPER dataset using their proposed model.

Implications and Future Directions

The implications of this research are multifaceted. On a practical level, the proposed framework enhances the reliability of segmentation outputs, crucial for applications where failure could have severe consequences, such as in autonomous vehicles or critical medical imaging tasks. The paper sets a precedent for further exploration of time-dynamic approaches in machine learning, encouraging future research to embrace temporal data structures in uncertainty quantification.

Looking ahead, potential developments may include the integration of more complex temporal models and the incorporation of additional uncertainty measures to further refine meta regression and classification tasks. Furthermore, extending the applicability of these methods to other domains beyond automated driving represents a promising direction, potentially contributing to the broader field of temporal deep learning approaches.

Overall, this paper significantly advances the state of semantic segmentation networks by incorporating a time-dynamic perspective, leveraging the rich and informative nature of temporal data to improve prediction reliability in crucial applications.